{
 "metadata": {
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.7 64-bit"
  },
  "language_info": {
   "name": "python",
   "version": "3.7.7",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "interpreter": {
   "hash": "e41d4561028816b5428aebb83a83c059bdf12e0224e471a2c21e6a82f3a5abf0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from simulations import *\n",
    "# here we first call function for generating random 1000 random sequences where we will mutate each one of them with 0-0.3 error rate.\n",
    "# !rm -rf simulations \n",
    "!mkdir simulations_chr\n",
    "!mkdir simulations_chr/fas/\n",
    "path = r'simulations_chr/fas/'\n",
    "generate_sequences(path, chr_ = '>chr1')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "mkdir: cannot create directory ‘simulations_chr’: File exists\n",
      "mkdir: cannot create directory ‘simulations_chr/fas/’: File exists\n",
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     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# now we call biser search, there is special mode for this simulation for speed purposes\n",
    "import os\n",
    "# !module load samtools\n",
    "path = r'simulations_chr/fas/'\n",
    "\n",
    "for i in os.listdir(path):\n",
    "    !echo '{path}/{i}'\n",
    "    !samtools faidx '{path}/{i}'\n",
    "!mkdir simulations_chr/seeds/\n",
    "!..exec/biser_search -m 1 simulations_chr/fas/ simulations_chr/seeds/"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/hiseric1/.local/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "[(-m, 1)]\n",
      "[simulations_chr/fas/, simulations_chr/seeds/]\n",
      "[simulations_chr/fas/, simulations_chr/seeds/]\n",
      "pair:  (simulations_chr/fas/1.fa, simulations_chr/fas/1_sds.fa)\n",
      "Finding SDs...\n",
      "whole code took 0.000562452s\n",
      "\u001b[1mIOError: \u001b[0mfile simulations_chr/fas/1.fa.fai could not be opened\n",
      "\n",
      "\u001b[1mraised from:\u001b[0m \u001b[32m__init__\u001b[0m\n",
      "/project/home/hiseric1/.seq/lib/seq/stdlib/core/file.seq:13:13\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "!mkdir  simulations_chr/merged/\n",
    "!../exec/sedef merge simulations_chr/seeds/ simulations_chr/merged/"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "mkdir: cannot create directory ‘simulations_chr/merged/’: No such file or directory\n",
      "🍁  🐚    SEDEF cc194a7-dirty; arguments:  (SSE4.1) ../exec/sedef merge simulations_chr/seeds/ simulations_chr/merged/\n",
      "No such file: No such file or directory\n",
      "Done merging filesimulations_chr/seeds/\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "!cat simulations_chr/fas/*.fa > simulations_chr/main_fa.fa\n",
    "!samtools faidx 'simulations_chr/main_fa.fa'\n",
    "    "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "!../exec/align.sh simulations_chr simulations_chr/main_fa.fa simulations_chr/main_fa.fa\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "mkdir: cannot create directory ‘simulations_chr/aligned’: No such file or directory\n",
      "mkdir: cannot create directory ‘simulations_chr/log_align’: No such file or directory\n",
      "parallel: Error: Parsing of --jobs/-j/--max-procs/-P failed.\n",
      "Usage:\n",
      "\n",
      "parallel [options] [command [arguments]] < list_of_arguments\n",
      "parallel [options] [command [arguments]] (::: arguments|:::: argfile(s))...\n",
      "cat ... | parallel --pipe [options] [command [arguments]]\n",
      "\n",
      "-j n            Run n jobs in parallel\n",
      "-k              Keep same order\n",
      "-X              Multiple arguments with context replace\n",
      "--colsep regexp Split input on regexp for positional replacements\n",
      "{} {.} {/} {/.} {#} {%} {= perl code =} Replacement strings\n",
      "{3} {3.} {3/} {3/.} {=3 perl code =}    Positional replacement strings\n",
      "With --plus:    {} = {+/}/{/} = {.}.{+.} = {+/}/{/.}.{+.} = {..}.{+..} =\n",
      "                {+/}/{/..}.{+..} = {...}.{+...} = {+/}/{/...}.{+...}\n",
      "\n",
      "-S sshlogin     Example: foo@server.example.com\n",
      "--slf ..        Use ~/.parallel/sshloginfile as the list of sshlogins\n",
      "--trc {}.bar    Shorthand for --transfer --return {}.bar --cleanup\n",
      "--onall         Run the given command with argument on all sshlogins\n",
      "--nonall        Run the given command with no arguments on all sshlogins\n",
      "\n",
      "--pipe          Split stdin (standard input) to multiple jobs.\n",
      "--recend str    Record end separator for --pipe.\n",
      "--recstart str  Record start separator for --pipe.\n",
      "\n",
      "See 'man parallel' for details\n",
      "\n",
      "Academic tradition requires you to cite works you base your article on.\n",
      "If you use programs that use GNU Parallel to process data for an article in a\n",
      "scientific publication, please cite:\n",
      "\n",
      "  O. Tange (2018): GNU Parallel 2018, Mar 2018, ISBN 9781387509881,\n",
      "  DOI https://doi.org/10.5281/zenodo.1146014\n",
      "\n",
      "This helps funding further development; AND IT WON'T COST YOU A CENT.\n",
      "If you pay 10000 EUR you should feel free to use GNU Parallel without citing.\n",
      "\n",
      "Command exited with non-zero status 255\n",
      "Align time: 0:00.14\n",
      "grep: simulations_chr/log_align/*.log: No such file or directory\n",
      "(standard_in) 1: syntax error\n",
      "Single-core running time:  hours ( seconds)\n",
      "grep: simulations_chr/log_align/*.log: No such file or directory\n",
      "(standard_in) 1: syntax error\n",
      "Memory used:  MB\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from simulations import *\n",
    "path1 = 'simulations_chr/main_fa.fa.fai'\n",
    "path2 = 'simulations_chr/aligned/'\n",
    "output = 'simulations_chr/output.txt'\n",
    "threshold = 0.9\n",
    "calculate_coverages(path1, path2, threshold, output)\n",
    "print ('Done calculating')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{26: 981, 30: 900, 14: 1000, 17: 1000, 13: 1000, 22: 994, 24: 987, 15: 998, 19: 997, 3: 1000, 7: 1000, 9: 1000, 25: 993, 10: 1000, 4: 1000, 8: 1000, 18: 999, 16: 1000, 28: 959, 1: 1000, 29: 958, 11: 1000, 21: 989, 2: 1000, 5: 1000, 23: 988, 12: 1000, 27: 968, 20: 997, 6: 1000} 31637\n",
      "Done calculating\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import pandas as pd\n",
    "from plotnine import *\n",
    "\n",
    "df = pd.read_csv(output, sep=';', index_col=False)\n",
    "# print (df)\n",
    "(ggplot(df, aes(x='error', y='hits')) +\n",
    " geom_line() + \n",
    " ylim(800,1000) +\n",
    " labs(x = u\"Edit error $\\delta$\", y=\"SDs detected by BISER\", title=\"BISER's performance on simulated SDs\") +\n",
    " theme_classic() +\n",
    " theme(text=element_text(family=['Helvetica']))\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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}